Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.
We apply machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal and do not make use of its history. Surprisingly, machine learning identifies a signal emitted from the fault zone previously thought to be low‐amplitude noise that enables failure forecasting throughout the laboratory quake cycle. We infer that this signal originates from continuous grain motions of the fault gouge as the fault blocks displace. We posit that applying this approach to continuous seismic data may lead to significant advances in identifying currently unknown signals, in providing new insights into fault physics, and in placing bounds on fault failure times.
It remains unknown how the small strains induced by seismic waves can trigger earthquakes at large distances, in some cases thousands of kilometres from the triggering earthquake, with failure often occurring long after the waves have passed. Earthquake nucleation is usually observed to take place at depths of 10-20 km, and so static overburden should be large enough to inhibit triggering by seismic-wave stress perturbations. To understand the physics of dynamic triggering better, as well as the influence of dynamic stressing on earthquake recurrence, we have conducted laboratory studies of stick-slip in granular media with and without applied acoustic vibration. Glass beads were used to simulate granular fault zone material, sheared under constant normal stress, and subject to transient or continuous perturbation by acoustic waves. Here we show that small-magnitude failure events, corresponding to triggered aftershocks, occur when applied sound-wave amplitudes exceed several microstrain. These events are frequently delayed or occur as part of a cascade of small events. Vibrations also cause large slip events to be disrupted in time relative to those without wave perturbation. The effects are observed for many large-event cycles after vibrations cease, indicating a strain memory in the granular material. Dynamic stressing of tectonic faults may play a similar role in determining the complexity of earthquake recurrence.
[1] We investigate the physics of laboratory earthquake precursors in a biaxial shear configuration. We conduct laboratory experiments at room temperature and humidity in which we shear layers of glass beads under applied normal loads of 2-8 MPa and with shearing rates of 5-10 μm/s. We show that above~3 MPa load, acoustic emission (AE), and shear microfailure (microslip) precursors exhibit an exponential increase in rate of occurrence, culminating in stick-slip failure. Precursors take place where the material is in a critical state-still modestly dilating, yet while the macroscopic frictional strength is no longer increasing.
[1] Recent work in medical nonlinear acoustics has led to the development of refined experimental method to measure material elastic nonlinear (anelastic) response. The technique, termed dynamic acoustoelastic testing, has significant implications for the development of a physics-based theory because it provides information that existing methods cannot. It provides the means to dynamically study the velocity-strain and attenuation-strain relations through the full wave cycle in contrast to most methods that measure average response. The method relies on vibrating a sample at low frequency in order to cycle it through different levels of stress-strain. Simultaneously, an ultrasonic source applies pulses and the change in wave speed and attenuation as a function of the low frequency strain is measured. We report preliminary results in eleven room-dry rock samples. In crystalline rock, we expect that the elastic nonlinearity arises from the microcracks and dislocations contained within individual crystals. In contrast, sedimentary rocks may have other origins of elastic nonlinearity, currently under debate. A large quadratic elastic nonlinearity is observed in Berkeley blue granite, presumably due to microcracks and dislocation-point defect interactions. In sedimentary rocks that include limestones and sandstones we observe behaviors that can differ markedly from the granite, potentially indicating different mechanical mechanisms. We further observe changes in measured nonlinear coefficients that are wave-strain amplitude dependent. Ultimately we hope that the new approach will provide the means to quantitatively relate material nonlinear elastic behavior to the responsible features, that include soft bonds dislocations, microcracks, and the modulating influences of water content, temperature and pressure.
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